The month of July is special to us as we started International Journal of Performability Engineering in July 2005 and we have come a long way to present position. An update on IJPE has been provided by us on how IJPE has progressed during past ten years on page 510 of this issue. Also for the benefit of readers, we also provide the record for citations of the papers published in IJPE on page 538 of this issue. We believe IJPE has performed quite well.

Prognostics and Health Management (PHM) conferences have become regular annual conference series both in North America and China, since 2009. Europe has been promoting, participating and contributing to these events since the beginning. In 2013, a very successful conference, with more than 200 participants from all over the World, was held at Politecnico di Milano, Milano (Italy). Therefore, when Drs. Piero Baraldi and Francesco Di Maio, together with Professor Enrico Zio, stimulated me with the idea of bringing out a special issue on PHM based on the papers presented at the conference held in Milano, I could not but agree readily to the initiative. The present issue, therefore, is the outcome of the suggestion of colleagues at Politecnico di Milano.

Prognostics and Health Management (PHM), is an area that links failure mechanisms studies to system lifecycle management. Actually, prognostics is a discipline which relies on the phenomenon of failure modes, helps detect early signs of wear and aging, that eventually lead to failures. The signs of degradation are correlated with a damage propagation model.

The methodologies used in prognostics usually fall into categories of data-driven approaches, model-based approaches, and hybrid approaches. Data-driven techniques utilize monitored operational data related to system health. Data-driven approaches can be deployed quickly and are not so expensive.

A model-based prognostic incorporates physical models of the system into the estimation of the remaining useful life (RUL).

Hybrid approaches attempt to utilize the strength of both- data-driven approaches and model-based approaches. In reality, it is rare that the used approach is purely data-driven or purely model-based. Quite often the model-based approaches include some aspects of data-driven approaches and data-driven approaches use available information from models.

As is known, prognostics is a dynamic process where predictions are updated as often as more operational data become available during the operational life of the system being studied. The quality of prediction also changes with time and must be tracked and quantified. Prognostic performance evaluation is very important for a successful implementation of PHM system. Therefore, it is implied that research in this progressive area needs to be vigorously followed up to develop better and effective techniques with time and applied to such areas which defy the use of standard techniques.

The papers included in this issue have been carefully chosen to bring out the salient features of the methodologies of PHM and the areas of their realistic applications.

I would be failing in my duty in not recording the appreciation of Dr. Piero Baraldi, Dr. Francesco Di Maio and Professor Zio, who have worked hard to guarantee quality, respect deadlines and the standard of publication included herein. Last but not least, the team of reviewers, who helped maintain the standard of papers high, are worthy of our appreciation. I would also like to thank the authors whose contributions are included who helped maintain the time line. It is hoped that this issue of IJPE will generate further interest in PHM and provide impetus to research in this important area, and we hope to continue bringing out such special issues in the future as well.

Over years, the International Journal of Performability Engineering (IJPE) has been providing a highly professional and authoritative source of information in the field of performability, which in effect, is not only achieving high level of dependability characterised by optimizing the attributes like quality, reliability, maintainability, safety etc., but also maximizing sustainability by minimizing the material and energy requirements as well as creating minimum waste and environment pollution over the entire life time of systems, products or services.

Prognostics Health Management can in fact be viewed as an essential and important activity of overall performability improvement strategy. The present Special Issue aims to provide an overview of the latest developments in the area of Prognostics and Health Management (PHM) methods and their applications in different technologies. PHM takes past, present and (predicted) future information on the conditions of an equipment and use it to detect degradation, diagnose faults, and predict failures. PHM results are, then, used to guide condition-based and predictive maintenance strategies, with significant improvement of equipment availability and results in substantial savings. This is the reason why considerable attention is being given to the development of improved techniques for health monitoring, fault detection, diagnosis and prognosis.

The six papers collected in this special issue are extended and revised versions of selected works presented at the international Prognostics and System Health Management Conference, PHM 2013, that took place at Politecnico di Milano, Milan (Italy) on September 8-11, 2013. The conference was the 4th edition of the Prognostics and System Health Management Conference series. Close to 250 attendees from universities, research laboratories, industries and consultancy firms shared four days of intense technical and social activities, creating a stimulating and pleasant atmosphere of technical exchange.

The unifying motivation behind the selection of the works is that of reporting on the international advancements in model-based and data-driven PHM techniques, highlighting their capabilities, reliability and cost-effectiveness in diverse practical applications, such as energy production, train and railway, oil and gas, maritime and manufacturing industries.

The first paper by Takahito Kobayashi, Yasukuni Naganuma and Hitoshi Tsunashima shows the effectiveness of condition-monitoring techniques in limiting the drawbacks of components degradation in safety-relevant systems, such as train transportation systems. The authors propose an improvement of an existing condition-monitoring system installed on the Tokaido Shinkansen train line for track irregularity estimation by Inverse Analysis. The novelty is in the capability of track irregularity estimation by using a Kalman filter approach fed with measures taken from accelerometers placed on the car-body above the rear bogie. Thus, the effect of the bending mode of the car-body can be reduced and the condition-monitoring can be based only on the car-body motion. The second paper by Chengpeng Wan, Xinping Yan, Di Zhang, Jing Shi and Shanshan Fu concerns the problem of identifying the hazards of a newly designed system and providing information for its operation and health management. The authors propose a comprehensive framework based on the Analytical Hierarchy Process (AHP) for the identification and ranking of the safety critical factors. Then, measures for optimal health management are identified using a TOPSIS method to tackle the multi-objective and multi-criteria optimization problem that arises. The proposed framework is applied with success to a marine Liquefied Natural Gas (LNG)-diesel engine that is at its early stage of development in the Chinese shipping industry.

The third paper by Marc Hilbert, Christiane Küch and Karl Nienhaus addresses the problem of model-based fault detection in complex industrial systems. The novelty of the proposed approach is that it does not require a model of the entire industrial system. This allows to obtain a remarkable reduction of the computational time for early fault detection. Furthermore, the approach is shown to be able to isolate the cause of the fault and to perform well also when applied to the detection of faults during operational transients. The demonstration of the method is obtained by its successful application to a problem of fault detection in wind turbines.

The fourth paper by Victor Krymsky proposes a Control Theory (CT) model for in-depth understanding of system behaviour, The state-space evolution during the system life-cycle can be used for system reliability quantification and failure time prognosis. In particular, the proposed CT model is shown to be effective for systems with a significant lack of initial reliability information, e.g. the distributions of the components Time To Failure (TTF) variables. In fact, the CT model is shown to easily combine with Imprecise Prevision Theory (IPT) to introduce 'interval-valued probabilities' in the CT model and, thus, quantify the uncertainty of the reliability estimates even if the distributions of the TTFs are unknown or imperfectly known.

The fifth paper by Micaela Demichela, Roberta Pirani and Maria Chiara Leva focuses on hazards and Safety Integrity Levels (SILs) of complex systems. A method is proposed to account qualitatively and quantitatively for the human factors in the logical models of Quantitative Risk Analysis (QRA) techniques. The paper proposes the use of the Integrated Dynamic Decision Analysis (IDDA) framework in association with Task Analysis for identifying all the possible alternative states into which the system could evolve as a logical and temporal sequence of events (including explicitly human interactions). The application of the framework to an unforeseen accidental sequence of events for an hydraulic press shows that the performance of a safety instrumented system in the operational phase is influenced by many factors: in particular, not only the system design and the related testing and maintenance strategies should be taken into account, but also Human and Organizational Factors (HOFs) play a major role.

The sixth paper by Piero Baraldi, Francesca Mangili, Giulio Gola, Bent H. Nystad and Enrico Zio deals with the problem of assessing the health status of critical components in the process industry. Specifically, the authors propose a method for estimating process parameters which are, then, used for assessing the component degradation level. The method is based on a hybrid ensemble of data-driven and physics-based models. A local procedure is adopted to aggregate the different model outcomes, weighed by the models performances on similar training data. A successful application is chosen, with regards to real measurements taken on offshore choke valves located topside at different wells and undergoing degradation due to erosion.

As a final remark, we would like to thank the authors for their outstanding contributions and the reviewers for their hard, timely and professional work; we also wish to acknowledge that this special issue would have not been possible without the kind and sharp support of Prof. Krishna. B. Misra, Editor-in-Chief of the journal, who has given us the opportunity and the assistance necessary to put together such a collection of interesting works. To all of these people goes our sincere professional appreciation and personal gratitude.

Piero Baraldi (B.S. in nuclear engng., Politecnico di Milano, 2002; Ph.D. in nuclear engng., Politecnico di Milano, 2006) is Assistant Professor of Nuclear Engineering at the Department of Energy at the Politecnico di Milano (Italy). He is the current Chairman of the European Safety and Reliability Association, ESRA, Technical Committee on Fault Diagnosis. He is functioning as Technical Committee Co-chair of the European Safety and Reliability Conference, ESREL 2014, and he has been the Technical Programme Chair of the 2013 Prognostics and System Health Management Conference (PHM-2013). He is serving as editorial board member of the international scientific journals such as : Journal of Risk and Reliability and International Journal on Performability Engineering. He is co-author of 56 papers in international journals, 55 in proceedings of international conferences and 2 books. He serves as referee of 4 international journals. (Email: piero.baraldi@polimi.it)

Francesco Di Maio (B.Sc. in Energetic Engineering, 2004; M.Sc. in Nuclear Engineering, 2006; Double EU-China PhD in Nuclear Engineering, 2010) is Assistant Professor in Nuclear Power Plants at Politecnico di Milano (Milano, Italy). His research aims at developing efficient computational methods for improving a number of open issues relevant for dynamic reliability analysis, system monitoring, fault diagnosis and prognosis, and safety and risk analysis of nuclear power plants. In 2009-2010 he has been Research Fellow of the Science and Technology Programme (STFP) in China, financed by the European Commission, and spent 24 months of practical research at Tsinghua University (Beijing, China). In 2010, he has been appointed as Senior Researcher in City University of Hong Kong. He has published more than 50 articles in peer-reviewed international journals and international conferences proceedings. He is serving as Associate Editor of the International Journal of Performability Engineering. He is Chair of the Italian IEEE Reliability Chapter. (Email: francesco.dimaio@polimi.it )

Enrico Zio (Enrico Zio (B.Sc. in nuclear engng., Politecnico di Milano, 1991; M.Sc. in mechanical engng., UCLA, 1995; PhD, in nuclear engng., Politecnico di Milano, 1995; PhD, in nuclear engng., MIT, 1998) is Director of the Chair in Complex Systems and the Energetic Challenge of the European Foundation for New Energy of Electricite' de France (EDF) at Ecole Centrale Paris and Supelec, full professor, President and Rector's delegate of the Alumni Association and past-Director of the Graduate School at Politecnico di Milano, adjunct professor at University of Stavanger. He is the Chairman of the European Safety and Reliability Association ESRA, member of the scientific committee of the Accidental Risks Department of the French National Institute for Industrial Environment and Risks, member of the Korean Nuclear society and China Prognostics and Health Management society, and past-Chairman of the Italian Chapter of the IEEE Reliability Society. He is serving as Associate Editor of IEEE Transactions on Reliability and as editorial board member in various international scientific journals, among which Reliability Engineering and System Safety, Journal of Risk and Reliability, International Journal of Performability Engineering, Environment, Systems and Engineering, International Journal of Computational Intelligence Systems. His research focuses on the characterization and modeling of the failure/repair/maintenance behavior of components, complex systems and critical infrastructures for the study of their reliability, availability, maintainability, prognostics, safety, vulnerability and security, mostly using a computational approach based on advanced Monte Carlo simulation methods, soft computing techniques and optimization heuristics. He is author or co-author of five international books and more than 170 papers in international journals. (Email: enrico.zio@polimi.it)

This paper demonstrates the possibility to estimate the track irregularities of Shinkansen tracks using car-body motions only. In an inverse problem to estimate track irregularity from car-body motions, a Kalman Filter was applied to solve the inverse problem. This technique is utilized to estimate the track irregularity in longitudinal level. It can be concluded that the track irregularity estimation in longitudinal level is possible with acceptable accuracy for practical use with method.

Recent years, with the rapid development of world economy, energy consumption is sharply increasing and the environment is deteriorating. Liquefied Natural Gas (LNG), a clean renewable energy which can be used as ship fuel, is drawing attentions from more and more countries over the world. However, the conversion of the LNG-diesel dual fuel engine (DFE) in China as a new research is just in its infancy, therefore the operability and safety of the technology have to be further concerned. In view of this, taking the China inland's first transformed marine DFE GC6135ACz as an example, a risk assessment on the failures of DFE engine has been carried out by analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) method. Key factors for failures of DFE as well as the optimal risk control options (RCOs) have been obtained by expert survey data, so as to enhance the safety level of marine LNG-diesel DFEs.

For wind turbines, condition monitoring is an essential part of research. To minimize downtime of the plants and to prevent extensive damage, it becomes inevitable to detect faults at an early stage, especially, since plants are increasingly placed in poorly accessible areas, like deserts, arctic or offshore regions. The following paper concentrates on temperature monitoring and discusses an observer-based approach reconsidering the model’s accuracy and reliability for condition monitoring purposes. As novel approach the fault detection is focused on individual measuring points rather on the overall system. The presented model is developed as an equivalent thermal circuit. To minimize the difference between modeled and measured temperature curve (residual), a observer is implemented and configured. The advantages and disadvantages as well as the improvement of the presented approach compared to presently used methods are outlined.

In this paper we propose a novel approach to deriving uncertainty model in reliability applications. It is based on a state space representation of the system life cycle which is similar to techniques widely used in the control theory (CT). Such a model in turn allows applying CT methods (e.g., optimal control algorithms) to assess the reliability in case of high level of uncertainty. In particular, a CT-based model is effective even if the system performances cannot be described in terms of single-valued statistical characteristics. We demonstrate that in such a situation a CT-based model combined with the so-called ‘interval-valued probabilities’ becomes a very promising tool for reliability computations.

The study consists in devising a method to account qualitatively and quantitatively for the human factor in verifying the Safety Integrity Level (SIL) assigned to machinery. Two crucial aspects have to be taken into account modelling man-machine interaction in Quantitative Risk Analysis (QRA):

1. The need to include the human interaction in the logical model of QRAs techniques;
2. The quantification of the effect of human factors.

The efforts were thus aimed at defining an improved methodological framework encompassing the integration of Human and Organisational Factors (H&OF) into safety analysis by means of quantitative risk assessment schemes.
In the end, the Integrated Dynamic Decision Analysis (IDDA) was adopted, integrated to Task Analysis. This tool allows modelling the logic of a complex system; it provides a representation of all the possible alternative states into which the system could evolve as a real logical and temporal sequence of events. The proposed model is designed with the aim of transferring the IDDA philosophy to the in-depth study of the deviations that may occur during human implementation of operational procedures and to analyse their effects on system reliability.

In offshore oil platforms, choke valve erosion is a major issue. An indicator of the choke valve health state is the valve flow coefficient, which is a function of measured and allocated parameters. The allocated parameters are typically provided by a physics-based model which has been proved to be inaccurate for some operating conditions. As a consequence, inaccuracies are introduced in the evaluation of the health indicator, undermining the possibility of using it for prognostics. In this paper, we overcome this hurdle by resorting to hybrid modelling which integrates the physics-based model into an ensemble of data-driven models built using Kernel Regression (KR) methods. A local procedure which uses the historical performance of the physical and data-driven models is adopted to aggregate the different model outcomes. The proposed hybrid ensemble-based approach is verified on real measurements performed on offshore choke valves located topside at different wells.

Bio-fuels appear to be more environment friendly in comparison to fossil fuels, considering the emission of greenhouse gasses when consumed. In this paper, an attempt is made to study and compare the mechanical and volumetric efficiencies of bio-fuels, prepared from the blending of soya-bean and mustard oils with petrol. It is found that the out of the two soya-bean oil blends, 15-PRS shows the higher mechanical efficiency compared to 20-PRS at the engine loads of 2.5 Kg, 5 Kg and 7.5 Kg, while out of the two mustard oil blends, 20-PRM shows the higher mechanical efficiency compared to 15-PRM at the engine loads of 2.5 Kg, 5 Kg and 7.5 Kg. Out of the two mustard oil blends, 15-PRM shows the higher volumetric efficiency compared to 20-PRM at the engine loads of 2.5 Kg and 5.0 Kg.

Renewable energy is currently considered as the main solution to reduce greenhouse gas emission. This has led to great developments in the use of renewable energy for electricity generation. Among many renewable energy resources, tidal energy has the advantage of being predictable, particularly when compared to wind energy. Currently the UK is the world leader in extracting energy from the tide; an estimation shows a potential of 67 TWh per year. In order to ensure safe operation and prolonged life for tidal turbines, condition monitoring is essential. The technology for power generation using tidal turbines is new therefore the condition monitoring concept for these devices is yet to be established. Also, there is a lack of understanding of techniques suitable for health monitoring of the turbine components and support structure given their unique operating environment.
In this paper the condition monitoring of a tidal turbine is investigated. The objective is to highlight the need for condition monitoring and establish procedures to decide the condition monitoring techniques required, in addition to highlighting the impact and benefits of applying condition based maintenance. A model for failure analysis is developed to assess the needs for condition monitoring and identify critical components, after which a ‘symptoms analysis’ was performed to decide the appropriate condition monitoring techniques. Finally, the impact of condition monitoring on system reliability is considered.